The other loss function uses shared labels. If the number of words in a query is \(n\) , and if the intent of the query is store lookup, there are \(n\)  ones in the target labels. If the intent is not a store lookup, there are \(n\)  zeros in the target labels. The reason for having \(n\)  identical labels is that during inference, each word would have its own intent (which appears similar to the ideas in [1]}). But the intent is shared by all the words of a query and it helps learning and improves the precision as well as the F-1 score, as table 1 shows. We compare our algorithm BERT-Multitask with BERT-base for the store lookup NER task.
